The construction of online vectorized High-Definition (HD) maps is critical
for downstream prediction and planning. Recent efforts have built strong
baselines for this task, however, shapes and relations of instances in urban
road systems are still under-explored, such as parallelism, perpendicular, or
rectangle-shape. In our work, we propose GeMap ($\textbf{Ge}$ometry
$\textbf{Map}$), which end-to-end learns Euclidean shapes and relations of map
instances beyond basic perception. Specifically, we design a geometric loss
based on angle and distance clues, which is robust to rigid transformations. We
also decouple self-attention to independently handle Euclidean shapes and
relations. Our method achieves new state-of-the-art performance on the NuScenes
and Argoverse 2 datasets. Remarkably, it reaches a 71.8% mAP on the large-scale
Argoverse 2 dataset, outperforming MapTR V2 by +4.4% and surpassing the 70% mAP
threshold for the first time. Code is available at
this https URL

GeMap 是一种应用于构建在线矢量化高清地图的方法，通过学习欧氏空间的形状和关系，利用几何损失和自注意机制，实现了最先进的性能。

在线基于几何学的矢量高清地图构建

Online Vectorized HD Map Construction using Geometry

2D-to-3D reconstruction is an ill-posed problem, yet humans are good at
solving this problem due to their prior knowledge of the 3D world developed
over years. Driven by this observation, we propose NeRDi, a single-view NeRF
synthesis framework with general image priors from 2D diffusion models.
Formulating single-view reconstruction as an image-conditioned 3D generation
problem, we optimize the NeRF representations by minimizing a diffusion loss on
its arbitrary view renderings with a pretrained image diffusion model under the
input-view constraint. We leverage off-the-shelf vision-language models and
introduce a two-section language guidance as conditioning inputs to the
diffusion model. This is essentially helpful for improving multiview content
coherence as it narrows down the general image prior conditioned on the
semantic and visual features of the single-view input image. Additionally, we
introduce a geometric loss based on estimated depth maps to regularize the
underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset
show that our method can synthesize novel views with higher quality even
compared to existing methods trained on this dataset. We also demonstrate our
generalizability in zero-shot NeRF synthesis for in-the-wild images.

提出 NeRDi—— 一种基于 2D 扩散模型通用图像先验的单视角 NeRF 综合框架，借助自然语言模型引导扩散模型进行图像合成，结合估算的深度图的几何约束，优化重建的 NeRF 表示，进而实现高质量的全景图像生成。

NeRDi: 基于语言引导扩散的单视角 NeRF 综合技术作为通用图像先验

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as  General Image Priors

Label hierarchies are often available apriori as part of biological taxonomy
or language datasets WordNet. Several works exploit these to learn hierarchy
aware features in order to improve the classifier to make semantically
meaningful mistakes while maintaining or reducing the overall error. In this
paper, we propose a novel approach for learning Hierarchy Aware Features (HAF)
that leverages classifiers at each level of the hierarchy that are constrained
to generate predictions consistent with the label hierarchy. The classifiers
are trained by minimizing a Jensen-Shannon Divergence with target soft labels
obtained from the fine-grained classifiers. Additionally, we employ a simple
geometric loss that constrains the feature space geometry to capture the
semantic structure of the label space. HAF is a training time approach that
improves the mistakes while maintaining top-1 error, thereby, addressing the
problem of cross-entropy loss that treats all mistakes as equal. We evaluate
HAF on three hierarchical datasets and achieve state-of-the-art results on the
iNaturalist-19 and CIFAR-100 datasets. The source code is available at
this https URL

提出了一种学习层次感知特征的方法，该方法利用层次结构中的分类器并使用 Jensen-Shannon Divergence 和几何损失来训练，可以在维持 top-1 误差的情况下改善分类器错误，从而提高分类性能。

学习分层感知特征以减少错误严重程度

Learning Hierarchy Aware Features for Reducing Mistake Severity

In this paper, we address the problem of reconstructing an object's surface
from a single image using generative networks. First, we represent a 3D surface
with an aggregation of dense point clouds from multiple views. Each point cloud
is embedded in a regular 2D grid aligned on an image plane of a viewpoint,
making the point cloud convolution-favored and ordered so as to fit into deep
network architectures. The point clouds can be easily triangulated by
exploiting connectivities of the 2D grids to form mesh-based surfaces. Second,
we propose an encoder-decoder network that generates such kind of multiple
view-dependent point clouds from a single image by regressing their 3D
coordinates and visibilities. We also introduce a novel geometric loss that is
able to interpret discrepancy over 3D surfaces as opposed to 2D projective
planes, resorting to the surface discretization on the constructed meshes. We
demonstrate that the multi-view point regression network outperforms
state-of-the-art methods with a significant improvement on challenging
datasets.

该论文提出了一种基于点云的物体表面重建方法，使用编码器 - 解码器网络通过单张图片生成多种视角下的点云，并且使用几何损失函数提高表面拟合准确性。